Two approaches to characterize global dynamics are developed in this
dissertation. In particular, the concern is with nonlinear and chaotic time
series obtained from physical systems. The objective is to identify the
features that adequately characterize a time series, and can consequently be
used for fault diagnosis and process monitoring, and for improved control.
This study has two parts. The first part is concerned with obtaining a
skeletal description of the data using Cluster-linked principal curves (CLPC).